Abstract #0808

Age estimation from whole-body MR images: A proof-of-principle study

Thomas Küstner^1,2,3, Tobias Hepp², Karim Armanious^2,3, Konstantin Nikolaou⁴, Sergios Gatidis^2,4, and Bin Yang³

¹Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, ²Medical Image and Data Analysis (MIDAS), University Hospital Tübingen, Tübingen, Germany, ³Institute of Signal Processing and System Theory, University of Stuttgart, Stuttgart, Germany, ⁴Department of Radiology, University Hospital Tübingen, Tübingen, Germany

Age is one of the most important clinical parameters describing patients in a medical context. The chronological age (CA) does however not necessarily reflect the true underlying biological age (BA) which can depend on multiple factors such as lifestyle, social environment, medical history, genetics and ethnicity. It is therefore desirable to measure BA quantitatively and objectively. In this proof-of-principle study, we examine if CA can be estimated from whole-body MRI. We propose a novel deep learning architecture to perform an accurate CA estimation.

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